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http://dx.doi.org/10.5302/J.ICROS.2003.9.6.442

Parameter Identification of an Electro-Hydraulic Servo System Using a Modified Hybrid Neural-Genetic Algorithm  

곽동훈 (부산대학교 대학원 지능기계공학과)
이춘태 (부산대학교 대학원 지능기계공학과)
정봉호 (부산대학교 대학원 지능기계공학과)
이진걸 (부산대학교 기계공학부)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.9, no.6, 2003 , pp. 442-447 More about this Journal
Abstract
This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.
Keywords
modified hybrid neural-genetic parameter estimation algorithm; electro-hydraulic servo system;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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